ls Ideological Polarisation by Age Group Growing inEurope?
Tom O’Grady∗
August 11, 2022
Abstract
Prominent theories claim that young Europeans are increasingly socialist as well asdivided from their elders on non-economic issues. This paper asks whether age-basedpolarisation is really growing in Europe, using new estimates of the ideological positionsof different age groups in 27 European countries across four issue domains from 1981-2018. The young in Europe turn out to be relatively libertarian: more socially liberalthan the old in most countries, but also more opposed to taxation and governmentspending. These age divides are not growing either: today’s differences over socialissues and immigration are similar in size to the 1980s, and if anything are startingto fall. Analysis of birth cohorts points to persistent cohort effects and period effectsas the explanation for these patterns; there is little evidence that European cohortsbecome uniformly more right-wing or left-wing with age. Hence age-based polarisationneed not be a permanent or natural feature of European politics, but is dependent onthe changing social, political and economic climate.
∗Associate Professor of Political Science, University College London. t.o’[email protected]. This replacesa previous version posted under the title “Is Europe becoming a ‘Gerontocracy’? New Evidence on AgeCleavages in Europe since the 1980s”
1
Age is becoming increasingly important in European politics. By 2100 the over-65s will ac-
count for 31% of Europe’s population, compared to 20% today and 10% in the mid 1980s.1
Large age gaps have opened up for voting in countries such as Spain and the UK, along-
side stark differences in turnout across the continent (Blais and Rubenson 2013; Orriols and
Cordero 2016; Sloam, Ehsan, and Henn 2018; Smets 2012). Here I look beyond demographic
change, voting and turnout to investigate a fourth potential source of age divides: ideological
polarisation. As I detail below, some sociologists and political scientists argue that ideolog-
ical age gaps should be growing too, with young people’s economic precarity pushing them
leftwards on economics at the same time as older voters are moving rightwards on social
issues and immigration.
How true are such claims? Existing evidence is incomplete at best, covering only short
periods, a handful of countries, or a narrow set of issues. Instead, I use a dataset of over one
hundred survey questions from all major pan-European surveys from 1981-2018, aggregating
them into dimension-specific ideological scales for age groups. I report three key empirical
findings. First, there is ideological polarisation by age on non-economic issues, but it has
not risen since the 1980s. Second, on tax and spending an age gap opened up in the last
twenty years, but the young are actually more conservative than the old; they are relatively
libertarian rather than more socialist. Third, cohort and period effects are the principle
source of evolving age gaps, making polarisation volatile over time rather than permanent.
Ideological Polarisation by Age: Theory and Existing
Evidence
Compared to older generations, young Europeans face higher unemployment, more insecure
labour markets, lower wealth, as well as inferior pensions and access to housing. These
patterns emerged strongly over the past two decades (Bell and Gardiner 2019; Hausermann,
Kurer, and Schwander 2015; Huttle, Wilson, and Wolff 2015; Lennartz, Arundel, and Ronald
2016; Seeleib-Kaiser and Spreckelsen 2018). Moreover, many came of age during the financial
crisis and its aftermath, an experience that typically leaves cohorts more left-wing through-
out their lives (Giuliano and Spilimbergo 2014). As a result, some sociologists of youth have
predicted growing polarisation over economic issues between age groups, with the ‘prole-
tarianisation’ of youth causing more left-wing demands for redistribution and government
intervention (Bessant, Farthing, and Watts 2017; Cote 2014; Furlong and Cartmel 2007).
Other scholars predict growing age divides over non-economic issues, exemplified by the
1. Eurostat and WHO data: see here and here
2
Brexit referendum where 66% of the over-65s voted to leave the European Union and 69% of
18-30s voted to remain (Ehsan and Sloam 2020). The ‘silent revolution’ theory argues that
older people are more socially conservative because younger generations were socialised in a
more affluent and liberal era when social equality and post-material values were more salient,
and they are better-educated and clustered in diverse urban areas. If younger cohorts are
outpacing liberalisation amongst older people, this leads to growing polarisation (Inglehart
2008; Norris and Inglehart 2019; Grasso et al. 2019). Ross (2018) also finds that growing up
amidst European integration and increasingly multiracial societies has eroded nationalism
and opposition to immigration amongst the European young, further dividing them from
their elders. Norris and Inglehart (2019) argue that a backlash against these progressive
social changes has begun to occur amongst older voters, who are turning further against
immigration and other social issues, exacerbating polarisation.
Recent elections in countries such as Germany, Great Britain, Ireland and Spain seem
to support the view that generational ideological polarisation is growing, featuring strong
age differences in party support (Orriols and Cordero 2016; Sloam, Ehsan, and Henn 2018).
Indeed, journalists often take voting patterns as prima facie evidence of greater ideological
polarisation. In the late 2010s The Independent claimed that “politics is being taken over
by children with a pipe dream of returning to the socialism I know doesn’t work.”2 whilst
The Economist fretted about a rise in “millennial socialists”, who “suffer from naivety about
budgets, bureaucracies and businesses”3. On non-economic issues, culture wars are also said
to divide increasingly ‘woke’ young people from the elderly.
However alternative theoretical perspectives, emphasising positions in the life cycle rather
than cohort effects, do not predict growing ideological polarisation by age. In theory the
elderly always have a stronger interest in higher government spending on pensions and health-
care, and decreased spending on education (Mulligan and Sala-i-Martin 1999; Poterba 1997).
Some social psychologists also argue that social conservatism rises with age due to natural
changes in personality and cognition. Ageing may lead to lower openness to new ideas as
well as an increased preference for certainty over ambiguity, traits that are strongly related
to social conservatism (Cornelis et al. 2009). Whether due to self-interest or differences in
cognition, these theories suggest that the young and old always hold different views but there
is no reason for polarisation to change over time. Consistent with this, a re-analysis of Nor-
ris and Inglehart (2019)’s data by Schafer (2021) finds no evidence of growing generational
polarisation for authoritarian values.
Existing empirical work has focused mainly on explaining why mass opinion has changed
2. See here3. https://www.economist.com/leaders/2019/02/14/millennial-socialism, 14th Feb 2019
3
or why age gaps exist: age, period or cohort effects? Studies of postmaterial and social issues
typically report a society-wide liberalisation of attitudes – period effects – alongside very
substantial cohort effects causing the young to be more socially liberal than the old (Andor,
Schmidt, and Sommer 2018; Inglehart 2008; Norris and Inglehart 2019; Peterson, Smith,
and Hibbing 2020). On economic issues, small age divides exist over spending on pensions
and healthcare, with somewhat greater divisions over education (Busemeyer, Goerres, and
Weschle 2009; Cattaneo and Wolter 2009; Hess, Nauman, and Steinkopf 2017; Sorensen
2013). However, almost none of these studies focus on whether age-based polarisation in
Europe is rising or falling, or why this is. Many also cover only a single country, a single
year, or a very limited set of political issues. For example, both Norris and Inglehart (2019)
and Schafer (2021) rely on European Social Survey data from 2002-14 only, and Busemeyer,
Goerres, and Weschle (2009) examine four survey questions from 1996. A key reason for this
spotty coverage is that existing cross-national survey data is sparsely and unevenly available,
often forcing scholars to rely on only a handful of survey items from a single survey. Data
is missing for a lot of countries, years and issues. Even repeated cross-national surveys
appear only occasionally over time. This makes it impossible to assess long-term change in
ideological polarisation by age group using survey questions alone.
The debate about age-based ideological polarisation is, therefore, far from settled and
we know very little about its long-term evolution. This article takes an exploratory and
descriptive approach, measuring age differences across multiple issue dimensions and many
countries from 1981-2018. My main aims are to ask (1) whether ideological polarisation by
age is increasing, (2) for which, if any, issue dimensions this is true and (3) what explains
changes in age-based polarisation over time.
Data and Methods
I do this using a new method developed by Caughey, O’Grady, and Warshaw (2019) and
implemented via the dgo package in R (Dunham, Caughey, and Warshaw 2017), which con-
verts sets of survey questions into multidimensional scaled ideological positions for subgroups
within multiple European countries. It allows many different surveys, covering various time
periods, countries and issues, to be aggregated together into scales that have much wider
geographic and temporal coverage than is possible using individual survey questions alone.
Their approach begins by estimating item response theory (IRT) models using survey ques-
tions, but differs from previous IRT models in two respects. First, it estimates ideological
positions for age groups within each country, rather than individual survey respondents. This
facilitates estimating scales for age-country groups even with just a handful of survey ques-
4
tions, when a standard IRT model for individuals would fail due to lack of data. Second, it
smooths the estimated ideological positions using a hierarchical model so that country-years
with little or no survey data borrow information from other periods and places. This allows
the model to produce estimates in country-years when survey data are sparse, or unavail-
able altogether. These features surmount the problem of sparse unevenly-available survey
data that has prevented previous studies from examining long-term changes in ideological
polarisation by age group. The model used in this paper is virtually identical to Caughey,
O’Grady, and Warshaw (2019), which includes extensive information on its estimation and
validation, including the scales’ internal consistency. The Supplementary Information for
this contains a more substantial verbal description of the modelling approach.
Underlying the estimated scales are over 100 individual survey questions across a wide
range of issues. They are taken from every existing cross-national European survey, in-
cluding the European Social Survey, Eurobarometer, ISSP modules, Pew Global Attitudes
Survey and the World Values Survey from 1981-2018. These all collect high-quality random
probability samples of the relevant national populations, with survey weights. Twenty-seven
European countries are included.4 I follow Caughey, O’Grady, and Warshaw (2019) in es-
timating scales for four ideological dimensions, each one estimated from a different set of
survey questions. These questions are identical to theirs, with the addition of two new years
of survey data in 2017 and 2018 and a small number of additional items. The Supplementary
Information fully details all survey questions used.
The first scale is labelled social and refers to social and post-material issues such as the
environment, abortion, gender equality and LGBT rights. The second is immigration and
includes questions on immigration and nationalism. The third is labeled relative economic
issues and is akin to ‘mood’ (Stimson 1991), using questions about the economic policy
status quo, primarily levels of taxation and government spending. The fourth is absolute
economic issues, using questions that ask about economic policy principles, such as the
desirability of redistribution in theory. The differential policy status quo across countries
means that citizens’ positions on the relative scale, but not the absolute scale, may reflect
existing levels of taxation and government spending as much as ideological beliefs, making
it vital to separate the two concepts. A country could, over time, desire greater government
spending because spending has fallen rather than due to changing views on the desirability
of spending in theory, which is captured by the absolute measure. This distinction is also
justified by the empirical results of both this paper and Caughey, O’Grady, and Warshaw
4. Austria, Belgium, Bulgaria, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany,Great Britain, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Netherlands, Northern Ireland, Norway,Portugal, Slovakia, Slovenia, Spain, Sweden, Switzerland
5
(2019), which show that the two series are not correlated.
To maximise the availability of survey data for each observation, the scales are estimated
for two-year periods. They therefore show how left-wing or right-wing different age groups
have been every two years since the 1980s, across four issue dimensions and twenty-seven
European countries. With around one million individual survey items each and many param-
eters, there is a tradeoff between computational complexity and the number of age categories
for whom one can estimate ideological positions. I therefore estimated ideological positions
for six age categories of ten years each: those aged 18-27, 28-37, 38-47, 48-57, 58-67 and
68-77. The old are cut off at 77 so as to create groups of equal sizes. This is crucial in
allowing a comparison of cohorts over time, as shown below, and in practice there are few
survey respondents over 77. The estimates start in 1981-82 for social and absolute economic
issues, and in 1985-6 and 1989-90 respectively for relative economic issues and immigration,
as these are the first periods in which any survey data are available.
Ideological Polarisation over Time
Figure 1 shows how average ideological positions have evolved since 1981 for the six age
groups across all 27 countries.5 Higher scores on the ideological dimensions always represent
greater conservatism, e.g. opposition to redistribution, tax and spending, gender equality or
immigration. The scales are each identified by setting ideology to have a mean of zero and
variance of one across groups and periods.
Clear age differences on social issues are evident, alongside a broad liberalisation of
opinion for all age groups. Scores should be interpreted as standard deviations from the
overall mean of zero. Changes in social conservatism have been substantively large over
time: about 1.3 standard deviations for 68-77 year-olds from the early 1980s to the late
2010s. This is almost identical to the model-estimated difference between the average 68-
77 year-old in 2017-18 in Hungary – one of the most socially conservative countries – and
socially-liberal Germany. In the 2018 European Social Survey (ESS), which is in the scale,
30% of 68-77 year-olds in Hungary strongly disagreed with the statement “gay men and
lesbians should be free to live their life as they wish”, whereas just 2% did so in Germany.
However, my main interest here is in differences between age groups: polarisation. In the
average European country the ideological gap between the youngest and oldest age groups
5. These are posterior means for each age group, averaged across countries (without weighting by popula-tion): the model first outputs the position of each age group in each country. Effectively Figure 1 shows theideological position of age groups in the average country. Each model was estimated using four chains with2000 iterations each, with the first half of each chain as warmup. All conventional diagnostic tools, such asGelman-Rubin statistics, indicated that each model converged.
6
Absolute Economic Relative Economic
Social Immigration
1980 1990 2000 2010 1980 1990 2000 2010
−1.0
−0.5
0.0
0.5
1.0
−1.0
−0.5
0.0
0.5
1.0
Con
serv
atis
m
Age
18−27
28−37
38−47
48−57
58−67
68−77
Figure 1: Trends in average conservatism over time by age group and issue domain acrossEuropean Countries, 1981-82 to 2017-18 [lines = posterior means, shading = 95% credibleintervals, higher values = more conservative]
on social issues has barely changed over time, remaining relatively constant at around 1
standard deviation. Hungary again serves as a useful illustration, where in 2017-18 the
model-estimated difference between the youngest and oldest age groups was close to the
overall average of 1 (see Figure 2). In the 2018 ESS, 48% of Hungarian 68-77 year-olds either
7
agreed or strongly agreed with the same statement about LGBT rights above, compared to
26% of 18-27 year-olds. This shows that although age differences are unchanged, Europeans
have liberalised so much that a majority of all age groups in one of the most conservative
countries is not opposed to freedom for LGBT people, a point also made by Schafer (2021).
Polarisation certainly exists, but is not overwhelmingly large. Figure 1 also shows that the
youngest four age groups have become less distinguishable over time; the main difference
today is between the oldest citizens and all others. On immigration, a slightly smaller age
gradient has persisted as European countries slowly became more pro-immigration. Here
too, age differences have scarcely changed over time. Overall, ideological differences by age
on social issues and immigration are no larger today than they were in the 1980s.
Until recently young Europeans were somewhat more conservative than the old in abso-
lute terms, but age gaps have narrowed over the past decade, becoming very modest. For
relative economic conservatism age gaps widened from the late 1990s as the young became
more opposed to taxing and spending than the elderly. Although it narrowed a little over
the 2010s, at the end of the 2010s the age gradient in opinion on relative economic issues
remained almost as large as for immigration, but in the opposite direction. Millennials and
their younger counterparts would be better described as relatively libertarian rather than
relatively socialist: more socially liberal than older people, but also favouring smaller gov-
ernment. This finding may be surprising.6 In the Supplementary Information I show that
this aggregate measure faithfully reflects trends in the individual survey questions that com-
prise it; disaggregating by spending area does not alter these conclusions. Only on education
spending are young Europeans less conservative, but the gap has narrowed since the 1990s.
And as with social issues, there is substantial generational agreement: majorities of both the
young and old support higher pension spending, for instance.
To examine possible cross-country differences, Figure 2 plots estimates of the age gap
between the youngest and oldest groups for social issues and relative economic issues in
2017-18 and Figure 3 plots 95% credible intervals around them. Variation across countries
is mostly modest. In terms of mean differences, twenty of the twenty-seven countries occupy
the upper left libertarian quadrant, where the young are more liberal on social issues but
more conservative on tax and spending. Young people in Eastern European countries are
uniformly more libertarian, with generational differences on relative economic issues smaller
in Western and Southern Europe. Figure 3 shows that in only two countries can the youngest
and oldest not be distinguished statistically in their ideology on social issues, but in several
places they are not statistically different in terms of relative economic conservatism. No
country except Denmark, however, shows any evidence that the young prefer higher tax and
6. Although Barnes, Blumenau, and Lauderdale (2021) present similar findings (for the UK only)
8
AT
BE
BU
CY
CZDK
EST
FI
FR
DE
GB
GR
HU
IRL
IT
LA
LT
NL
NIRL
NO
PL
PT
SK
SI
ESP
SWE
SWI
0.0
0.5
1.0
1.5
−2 −1 0 1
Age Gap for Relative Economic Conservatism (Old − Young)
Age
Gap
for
Soc
ial C
onse
rvat
ism
(O
ld −
You
ng)
Figure 2: Age Differences in Conservatism (Old minus Young) by Country for Social Issuesand Relative Economic Issues, 2017-18 [Difference in posterior means between 68-77 year-olds and and 18-27 year-olds, by country. Positive = old people more conservative thanyoung people]
spending than the old. Figures S5 and S6 in the Supplementary Information show Figure 1
separately for Eastern and Western European countries. Although all age groups in Eastern
Europe have liberalised more slowly on non-economic issues – consistent with a later arrival
of the silent revolution there (Walczak, Van der Brug, and de Vries 2012) – age polarisation
has evolved almost identically in all regions across all dimensions. Whether one looks at
Europe as a whole, at individual countries or at European regions, the conclusions about
polarisation are the same.
9
10
−1
0
1
2
3
−4 −2 0 2
Age Gap for Relative Economic Conservatism (Old − Young)
Age
Gap
for
Soc
ial C
onse
rvat
ism
(O
ld −
You
ng)
Figure 3: 95% Credible Intervals for Age Differences in Conservatism (Old minus Young)by Country for Social Issues and Relative Economic Issues, 2017-18 [red=social issues,blue=relative economic issues]
Cohort Patterns
Why has age-based polarisation not grown over time? To answer this question I convert the
results for age groups into birth cohorts, using the fact that by looking ten years apart, we
observe the same cohort on multiple occasions. For example, those aged 18-27 in 1987-8 will
be 38-47 in 2007-8.7
I start with social issues, where age differences are largest. The evolution of each cohort’s
ideological position from 1987-8 to 2017-18 (mean positions and 95% credible intervals) is
shown in Figure 4. Some intra-cohort liberalisation is clear for all cohorts. However, within-
cohort change did not keep pace with overall change. The three generations for which we
have complete data liberalised by substantially less than the total change for age groups in
Europe over the period. On the other hand, diagonal comparisons across periods and cohorts
show different cohorts at the same ages. The 1960-70 cohort was aged 18-27 in 1987-8 with
an estimated ideology of -0.014, compared to the 1970-80 cohort who were 18-27 in 1997-8
with an estimated ideology of -0.53. This shows that in addition to within-cohort change
there have been large cohort replacement effects. Each cohort began its political life more
liberal than its predecessor and maintained this distinctiveness over time.
Thus age gaps in ideology on social issues have persisted (Figure 1) because despite all
cohorts liberalising over time, each new cohort has also been persistently more socially liberal
than its predecessor. The combination of within-cohort and cohort replacement effects left
polarisation between different age groups roughly unchanged. There is some evidence that
these cohort effects are slowing down. Younger cohorts are more homogenous than older
ones, beginning their political lives closer to the middle-aged than in the past. This helps
explain why, in Figure 1, the greatest age difference is observed between the oldest citizens,
and the middle-aged and below.
Figure 4 unambiguously demonstrates large cohort effects, but aggregate data on cohorts
cannot separate out age and period effects as explanations for within-cohort change. Theo-
retically, period effects are the more likely explanation. Existing theories of ageing predict
greater conservatism as a natural feature of ageing, and that age effects should primarily
kick in as people go past middle age. Yet Figure 2 shows that all cohorts became more
7. Strictly speaking, there is some very slight overlap at the edges of these groups due to the use of two-yearperiods to estimate ideological positions: someone born in 1960 observed in 1987-8 could appear in eitherthe 1950-60 cohort (aged 27 in 1987) or in the 1960-70 cohort (aged 28 in 1988). However such effects areminimal. This also assumes no differential mortality between left-wingers and right-wingers within cohorts(Rodriguez 2018). However wealth (and therefore life expectancy) may be positively related to economicconservatism and negatively related to social conservatism. For social issues and immigration, therefore, thepatterns in this paper might slightly over-state the contribution from within-cohort change and under-statethe contribution from cohort replacement, because social conservatives die younger.
11
−1.0
−0.5
0.0
0.5
1.0
1.5
1987−8 1997−8 2007−8 2017−18
Soc
ial C
onse
rvat
ism
Cohort
1910−20
1920−30
1930−40
1940−50
1950−60
1960−70
1970−80
1980−90
1990−2000
Figure 4: Social Conservatism by Birth Cohort in Europe, 1987-8 to 2017-18 [higher values= more conservative, grey shading = 95% credible intervals]
left-wing on social issues, and that this continued when cohorts became elderly. It was not
the case that the elderly turned more conservative while the young became more liberal.
Period effects, as the prevailing social climate became more socially liberal, are a much more
plausible explanation for why all cohorts liberalised throughout their lives.
Figures 5 and 6 do the same exercise for immigration and relative economic issues for
1989-9 to 2009-10 and 1987-8 to 2017-18 respectively, because the immigration data do not
start until 1989-90. For immigration, within-cohort changes were mostly negligible, although
since 2000 there was some increased conservatism amongst older cohorts, consistent with a
backlash against Europe’s prevailing liberal consensus on the issue (Norris and Inglehart
2019). As with social issues, cohort replacement effects were large and consequential, with
new cohorts more pro-immigration than their predecessors at equivalent ages.
For relative economic issues, within-cohort changes were larger than for either immigra-
tion or social issues, and their direction also fluctuated over time. Cohorts tended to change
together over time, but behaved differently at equivalent ages. The 1950-60 cohort became
more conservative over the 1987/88 to 1997/98 period as they aged from 28-37 to 38-47
12
−1.0
−0.5
0.0
0.5
1.0
1989−90 1999−00 2009−10
Imm
igra
tion
Con
serv
atis
m Cohort
1912−22
1922−32
1932−42
1942−52
1952−62
1962−72
1972−82
1982−92
Figure 5: Immigration Conservatism by Birth Cohort in Europe, 1989-90 to 2009-10 [highervalues = more conservative, grey shading = 95% credible intervals]
whilst between the same ages, the 1970-80 cohort became more left-wing from 2007/8 to
2017/18. In other words, period effects operating on all cohorts at once were dominant.
Effects of ageing, on the other hand, would imply that at equivalent ages cohorts changed
in the same way. This is intuitive: changes in the size of governments as well as economic
booms and busts occur more often and more rapidly than the kinds of social change that
influence social conservatism. Since the mid-1990s new cohorts such as Millennials did in
fact begin their political lives more left-wing than previous cohorts were at an equivalent
age: compare the 1970-80 cohort at ages 18-27 (in 1997-8) to the 1980-90 cohort at the
same age (in 2007-8). But within-cohort change has outpaced the impact of these cohort
replacement effects. Older cohorts became so much more left-wing over the financial crisis
and its aftermath that at a given time, older age groups were more left-wing. Period effects
– but amongst groups older than millennials – have been the predominant influence.
13
−1.0
−0.5
0.0
0.5
1.0
1987−8 1997−8 2007−8 2017−18
Rel
ativ
e E
cono
mic
Con
serv
atis
m
Cohort
1910−20
1920−30
1930−40
1940−50
1950−60
1960−70
1970−80
1980−90
1990−2000
Figure 6: Relative Economic Conservatism by Birth Cohort in Europe, 1987-8 to 2017-18[higher values = more conservative, grey shading = 95% credible intervals]
5. Conclusion
This paper carried out a comprehensive examination of age differences in ideologies in Europe
since the 1980s, addressing unresolved theoretical and empirical debates. Some sociologists
and political scientists such as Cote (2014) and Norris and Inglehart (2019) have predicted
widening generational gaps on both economic and social issues, with the media depicting
young Europeans as increasingly socialist and ‘woke’ in the wake of widening age gaps in
terms of vote choice. Other scholars have cast doubt on this, whether taking issue with
previous empirical analyses (Schafer 2021) or arguing that age rather than cohort effects
dominate (e.g., Mulligan and Sala-i-Martin 1999). My analysis helped resolve these debates
by extending the analysis of age polarisation over a much longer time period, more issue
dimensions, and more countries than any previous study.
I found no evidence that the young and old are becoming increasingly ideologically op-
posed. Nor is it true that young Europeans today are much more socialist than the elderly,
or that age divisions over ‘woke’ issues are wider than in the past. In most places the young
14
are more opposed to tax and government spending than the elderly. Today’s age divides over
social issues and immigration are similar in size to the 1980s and if anything are starting
to fall, as the young and middle-aged become more similar. And despite these age gaps, on
issues such as LGBT rights and pension spending, majorities of the young and old support
the same policies. However, polarisation has not remained fairly constant for non-economic
issues due to age effects being dominant: there is little evidence that ageing naturally leads
to greater conservatism. Today’s age divides have arisen from a sometimes complex inter-
action of cohort and period effects, with cohort effects especially important for social issues
and immigration. Hence age-based polarisation need not be a permanent or natural feature
of European politics, but depends on changing social, political and economic climates. If
cohort replacement effects for social issues continue to slow down, age divides will fall.
If ideological age gaps are largely unchanged, why have age gaps for voting widened
recently? A likely explanation is that the emergence of new parties with more extreme
positions on social issues and immigration over the past thirty years – and more emphatic
communication of these stances – has helped the young and old to better express their long-
standing non-economic differences when voting. Age divides might appear to have grown
due to the actions of parties, but in reality young and old voters in Europe are not more
polarised than in the past.
15
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18
Supplementary Information for “ls Ideological Polarisation by Age
Group Growing in Europe?”
19
1 Explanation of the Modelling Procedure
Readers interested in the technical details of the model, its estimation, and validation of the
resultant scales, are referred to Caughey, O’Grady and Warshaw (2019). As explained below,
it contains very extensive validation exercises that justify a four-dimensional structure for
European opinion and that establish the internal consistency of each scale.
1.1 How similar is this paper’s modelling approach to Caughey,
O’Grady and Warshaw (2019)?
The modelling approach is the same as theirs, with one minor change: whereas they estimate
opinion for three age groups and two genders, I estimate opinion for six age groups. It is
computationally very difficult to estimate for many more than six demographic groups, and
in some cases the number of responpents in a given country-year would be too small for
certain groups defined by age and gender. The model that produces these scales is identical
in both papers, as is the R package that implements it.
This paper also uses slightly more data than Caughey, O’Grady and Warshaw (2019):
mainly due to extending their data from 2016 to 2018, but also because a few additional
items that were missed in their paper (such as opinions about welfare provision in the ESS)
were also added.
A comparison of Figure 2 in their paper to Figure 1 in this paper shows that the two
approaches produce near-identical results, as should be expected.
1.2 Why is this methodological approach needed? Why not use
other approaches?
The core problem solved by this methodological approach is that existing survey data is
sparsely available across both time and countries. The same question, for instance on redis-
tribution, might appear only in five years for the whole 1981-2018 period, and even in those
years it is unlikely to be available across all European countries. This sparseness presents
three key problems that the Caughey, O’Grady and Warshaw model solves.
First, a naive model of ideological change would be difficult to interpret. Suppose, for
example, that one simply measured conservatism in each available year by taking the propor-
tion of left- or right-wing responses across all questions asked in that year. Such a measure
would be sensitive to differences in baseline support across questions when questions appear
unevenly. Suppose that in different years, 10% of respondents in a survey in year t disagreed
that “it should be the government’s responsibility to provide healthcare for the sick,” but
20
50% of respondents in a survey in year t+2 disagreed that “it should be the government’s
responsibility to provide a decent standard of living for the unemployed.” Suppose further
that we tried to use the percentage disagreeing with each one as a measure of conservatism
in each year. Then, we could not tell whether the difference in opposition was due to a
genuine rise in conservatism, or because equally-conservative respondents prefer supporting
sick citizens to supporting the unemployed.
Second, this problem potentially motivates the use of more nuanced scaling techniques
such as a standard IRT model or principle component analysis, but these approaches are also
impossible with standard cross-national surveys. In most years, there are at best a handful
of questions per respondent on any particular dimension, but an IRT model estimated for
individual respondents requires many more responses for estimation.
Third and most crucially, some time periods feature either no survey questions at all,
a tiny handful of questions, or a tiny handful of countries. A naive model would simply
produce no estimates for these country-years (in the case of wholly missing data), or it
would be very unstable and sensitive to the actual questions asked (in the case of only one
or two questions). Because a large number of country-years are missing any data at all, there
would be a lot of missingness in any estimates of cross-national/over-time ideologies. This
is conceptually similar to the familiar problem of missing responses within a given survey.
Indeed, one way of thinking of the Caughey-O’Grady-Warshaw model is that in years where
data are very sparse or non-existent, it imputes the missing data in a similar fashion to
multiple imputation procedures.
1.3 How does the method work? How does it solve the problems
presented by sparse and unevenly-available data?
The model essentially contains two main components.
1. The first is an ordinal IRT model of ideology (within each of the four issue domains)
for age groups within countries rather than for individual survey respondents. In a
standard way, this assumes that for a given survey question, the probability of an in-
dividual from a particular age group selecting level k of a question’s response scale is
positively related to that group’s conservatism as well as the question-specific thresh-
old for a conservative response, with the “discrimination” of each survey item (i.e., the
strength of its relationship to conservatism) also estimated. The number of individuals
in a given group giving a particular response is then estimated with a standard multi-
nomial sampling model, adjusted for survey weights, implying a likelihood function
and hence estimates of country-age-group ideological positions.
21
2. The second stage takes these country-age-group estimates and smooths them across
time and countries using a hierarchical model for the group means. The magnitude
of change between years is constrained by a prior that predicts the group’s ideological
position based on its value in adjacent time periods. The final posterior estimates of
ideology are a compromise between this prior and the likelihood implied by stage 1.
This overcomes the three problems mentioned above:
1. Estimating the relationship of each question to latent conservatism greatly reduces the
model’s sensitivity to which questions are asked when.
2. Although each individual answers only a few questions in each survey, there are many
survey responses from each group (e.g., 18-27 year-olds in Germany), making an IRT
model at the level of groups feasible.
3. The hierarchical model facilitates estimation of ideology when data is sparse. When a
lot of survey data are available for a given country-year, the likelihood will be given
more weight by the model. With less survey data, the prior gets more weight. If no
survey data are available at all, this prior acts as a predictive model that imputes
ideology.
1.4 How do we know that the scales have the same meaning over
time?
This is, of course, a problem that can affect any long-term measure of conservatism, whether
looking only at individual survey questions or using alternative algorithms such as Stimson’s
dyad ratios procedure. Different survey items might give a different picture of conservatism,
and they are unevenly available over time and space.
The use of an IRT model helps deal with the concern about different questions having
different meanings for responents (or to put it in the language of IRT models: each ques-
tion has a different relationship to latent conservatism; some survey items are better than
others at discriminating between left- and right-wing groups). IRT models such as the one
used in the paper deal with that problem by directly estimating the discrimination of each
survey item. That means that the model is sensitive to which questions are being asked
when: the threshold parameters governing the relationship between survey items and latent
conservatism are different for each survey question, so that not every survey item ‘affects’
the scales in the same way.
A more technical issue is whether or not the model should allow these threshold parame-
ters to evolve over time, capturing the notion that the relationship between survey items and
22
latent conservatism might be different at different times. This more flexible model would
come with significant downsides however. First, it would mean that the substantive meaning
of ‘conservatism’ is not fixed over the course of the estimation. We could not say whether
a given country-age group has become more conservative over time, only whether they have
become more or less conservative in terms of their alignment with prevailing definitions of
‘conservatism’ at given times. Second, such a model would be likely to under-estimate ide-
ological change by attributing some of it to changes in the item thresholds. During their
review process, Caughey, O’Grady and Warshaw also carried out extensive comparisons of
model results with fixed and evolving thresholds. These yielded substantially almost identi-
cal results, suggesting that the assumption of fixed thresholds is empirically defensible. For
those reasons the Caughey-O’Grady-Warshaw model uses fixed threshold parameters that
do not change over time and in effect pre-suppose that ‘social conservatism’, for instance,
means the same thing in the 1980s as in the 2010s. This is not unreasonable: someone who
opposes abortion and gender equality would be considered conservative in both periods.
1.5 How do we know that survey items within the four domains
‘belong’ together? Why these four scales and not others?
As Caughey, O’Grady and Warshaw (2019: 678-9) put it, “we emphasize that our catego-
rization of questions [into scales] was based on ex ante substantive judgment and not on
statistical criteria for selecting the “correct” number of latent dimensions, making it anal-
ogous to confirmatory rather than exploratory factor analysis.” This makes sense because
there is surely no single correct answer to the question: “how many issue dimensions are
there in European politics?” Rather, there are more and less useful ways to summarise
conservatism in terms of underlying sets of survey questions.
Nonetheless, their measures are derived explicitly from substantive theories about the
content of different issue dimensions, as theorised by scholars such as Ronald Inglehart,
Herbert Kitschelt and Hanspeter Kriesi. Theories of (for instance) post-material values
state that opinions on issues as diverse as marijuana legalisation, abortion rights and same-
sex marriage are all related to an underlying socially conservative/socially liberal dimension
and should be expected to move together over time, for instance in line with economic
development.
The fact that all four scales are poorly correlated over time provides a clear empirical
justification for separating them.8 Notably, they show that their results for the social issues
8. Although across Europe as a whole, immigration and social conservatism move together, this is nottrue of individual countries: see Caughey, O’Grady and Warshaw 2019, p. 684.
23
scale are unaffected by removing items related to the environment, which are classified as
‘postmaterial’ issues by Inglehart but might be thought of as theoretically unrelated to issues
such as gender equality and LGBT rights. Moreover, they also provide extensive confirmatory
factor analysis of all the scales in their Supplementary Information. Their analysis, including
scree plots, demonstrates that each scale is explained primarily by a single dimension. And
within each scale, virtually all survey items are positively correlated with each other. These
provide further empirical justification for the scales.
1.6 What other problems might exist with these scales?
There are a number of potential limitations, all of which could affect the cross-country
or over-time comparability of all cross-national survey data or measures derived from it,
including our scales and previous measures of European ideology such as self-placement on
a left-right scale. These limitations include: differences in sampling procedures or survey
response patterns that lead to measured cross-country or over-time differences in opinion in
the absence of genuine differences; differential item functioning, such that different people in
different countries or periods interpret the same questions differently; or differential influence
from the policy ‘status quo’ across countries (whereby, for instance, equally-conservative
respondents respond differently to the question “should gays and lesbians be free to live
their lives as they wish?” based on the prevailing levels of LGBT rights in their country).
24
2DetailsofSurv
eyItemsIn
cluded
inth
eM
odels
Key
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ts:
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tern
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amily
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uro
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EV
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alues
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25
Tab
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ore
imp
orta
nt,
free
dom
or3-
poi
nt
Con
tin
ued
onn
ext
page
27
Tab
le1
–C
onti
nu
edfr
ompr
evio
us
page
Var
iable
Surv
eyY
ears
Ques
tion
Wor
din
gR
esp
onse
Opti
ons
Nam
eC
over
ed
1999
-00,
2008
-10
equal
ity?
1=
equal
ity,
3=
free
dom
concu
mp
EV
S19
90,
1999
-00,
To
what
exte
nt
do
you
feel
conce
rned
abou
t5-
poi
nt
2008
-10
the
livin
gco
ndit
ions
ofth
eunem
plo
yed?
1=
very
much
,5
=not
atal
l
diff
nec
ISSP
INE
Q19
87,
1992
,In
order
toge
tp
eople
tow
ork
har
d,
do
you
4-p
oint
2009
thin
kla
rge
diff
eren
ces
inpay
are
nec
essa
ry?
1=
defi
nit
ely
not
,4
=ab
solu
tely
incp
rosp
ISSP
INE
Q19
87,
1992
,D
oyo
uag
ree
ordis
agre
ew
ith
thes
e5-
poi
nt
1999
stat
emen
ts..
.“la
rge
diff
eren
ces
inin
com
e1
=st
rongl
ydis
agre
e,5
=st
rongl
yag
ree
are
nec
essa
ryfo
ra
countr
y’s
pro
sper
ty”
unip
oor
ISSP
INE
Q19
87,
1992
Ple
ase
show
how
much
you
agre
eor
5-p
oint
dis
agre
ew
ith
thes
est
atem
ents
...“
the
1=
stro
ngl
yag
ree,
5=
stro
ngl
ydis
agre
e
gove
rnm
ent
shou
ldpro
vid
em
ore
chan
ces
for
childre
nfr
omp
oor
fam
ilie
sto
goto
univ
ersi
ty”
resp
job1
ISSP
INE
Q19
87,
1992
[as
abov
e]..
.“th
ego
vern
men
tsh
ould
pro
vid
e5-
poi
nt
ajo
bfo
rev
eryo
ne
who
wan
tson
e”1
=st
rongl
yag
ree,
5=
stro
ngl
ydis
agre
e
resp
um
p1
ISSP
INE
Q19
87,
1992
[as
abov
e]..
.“th
ego
vern
men
tsh
ould
pro
vid
e5-
poi
nt
2009
adec
ent
stan
dar
dof
livin
gfo
rth
eunem
plo
yed”
1=
stro
ngl
yag
ree,
5=
stro
ngl
ydis
agre
e
bas
icin
cIS
SP
INE
Q19
87,
1992
[as
abov
e]..
.“th
ego
vern
men
tsh
ould
pro
vid
e5-
poi
nt
ever
yone
wit
ha
guar
ante
edbas
icin
com
e”1
=st
rongl
yag
ree,
5=
stro
ngl
ydis
agre
e
taxri
ch1
ISSP
INE
Q19
87,
1992
,D
oyo
uth
ink
that
peo
ple
wit
hhig
hin
com
es5-
poi
nt
1999
,20
09sh
ould
pay
ala
rger
shar
eof
thei
rin
com
ein
taxes
1=
much
larg
ersh
are
than
thos
ew
ith
low
inco
mes
,th
esa
me
shar
eor
5=
much
smal
ler
shar
e
asm
alle
rsh
are?
Con
tin
ued
onn
ext
page
28
Tab
le1
–C
onti
nu
edfr
ompr
evio
us
page
Var
iable
Surv
eyY
ears
Ques
tion
Wor
din
gR
esp
onse
Opti
ons
Nam
eC
over
ed
pri
vent
ISSP
EN
V19
93,
2000
,H
owm
uch
do
you
agre
eor
dis
agre
ew
ith
the
5-p
oint
2010
follow
ing
stat
emen
ts..
.“pri
vate
ente
rpri
seis
1=
stro
ngl
ydis
agre
e,5
=st
rongl
yag
ree
the
bes
tw
ayto
solv
em
yco
untr
y’s
pro
ble
ms”
free
com
pE
BV
AL
2006
,20
08,
For
each
ofth
efo
llow
ing
pro
pos
itio
ns,
tell
me
4-p
oint
2009
ifyo
uag
ree
ordis
agre
e..
.“F
ree
com
pet
itio
nis
1=
tota
lly
dis
agre
e,4
=to
tally
agre
e
the
bes
tgu
aran
tee
for
econ
omic
pro
sper
ity
“
eqju
stE
BV
AL
2006
,20
08,
[as
abov
e]..
.“W
enee
dm
ore
equal
ity
and
just
ice
4-p
oint
2009
even
ifth
ism
eans
less
free
dom
for
the
indiv
idual
”1
=to
tally
dis
agre
e,4
=to
tally
agre
e
govre
dis
t3E
BP
OV
2009
,20
10F
orea
chof
the
follow
ing
pro
pos
itio
ns,
tell
me
4-p
oint
ifyo
uag
ree
ordis
agre
e..
.“th
ego
vern
men
t1
=to
tally
agre
e,4
=to
tally
dis
agre
e
shou
lden
sure
that
the
wea
lth
ofth
eco
untr
yis
redis
trib
ute
din
afa
irw
ayto
all
citi
zens”
nofi
ght
EB
PO
V20
02,
2010
[as
abov
e]“T
her
eis
no
poi
nt
intr
yin
gto
figh
t4-
poi
nt
pov
erty
,it
will
alw
ays
exis
t”1
=to
tally
dis
agre
e,4
=to
tally
agre
e
diff
nec
1E
BP
OV
2009
,20
10[a
sab
ove]
“Inco
me
ineq
ual
itie
sar
enec
essa
ry4-
poi
nt
for
econ
omic
dev
elop
men
t”
1=
tota
lly
dis
agre
e,
som
ear
ep
oor
4=
tota
lly
agre
e
free
mkt
PE
W20
02,
07,
08,
09M
ost
peo
ple
are
bet
ter
offin
afr
eem
arke
t4-
poi
nt
econ
omy,
even
thou
ghso
me
peo
ple
are
rich
1=
com
ple
tely
dis
agre
e,
and
som
ear
ep
oor
4=
com
ple
tely
agre
e
inte
rfer
eP
EW
2002
,09
,11
,12
what
’sm
ore
imp
orta
nt
in(s
urv
eyco
untr
y)
4-p
oint
soci
ety
–th
atev
eryo
ne
be
free
topurs
ue
1=
opti
on2,
2=
opti
on1
Con
tin
ued
onn
ext
page
29
Tab
le1
–C
onti
nu
edfr
ompr
evio
us
page
Var
iable
Surv
eyY
ears
Ques
tion
Wor
din
gR
esp
onse
Opti
ons
Nam
eC
over
ed
thei
rlife
’sgo
als
life
’sgo
als
wit
hou
t
inte
rfer
ence
from
the
stat
e,or
that
the
stat
epla
yan
acti
vero
lein
soci
ety
soas
to
guar
ante
eth
atnob
ody
isin
nee
d?
Table
S2:Variablesincluded
inth
eRelativeEco
nomic
Sca
le
Var
iable
Surv
eyY
ears
Ques
tion
Wor
din
gR
esp
onse
Opti
ons
Nam
eC
over
ed
cuts
pen
dIS
SP
RO
G19
85,
1990
,19
96,
Infa
vour
orop
pos
ecu
tsin
5-p
oint
2006
,20
16go
vern
men
tsp
endin
g1
=st
rongl
yag
ainst
,5
=st
rongl
yin
favo
r
less
reg
ISSP
RO
G19
85,
1990
,19
96,
Infa
vour
orop
pos
ele
ss5-
poi
nt
2006
,20
16go
vern
men
tre
gula
tion
sof
1=
stro
ngl
yag
ainst
,5
=st
rongl
yin
favo
r
busi
nes
s
spen
dhea
lth
ISSP
RO
G19
85,
1990
,19
96,
Lis
ted
bel
owar
eva
riou
sar
eas
5-p
oint
2006
,20
16of
gove
rnm
ent
spen
din
g.P
leas
e1
=sp
end
much
mor
e,5
=sp
end
much
less
show
whet
her
you
wou
ldlike
to
see
mor
eor
less
gove
rnm
ent
spen
din
gin
each
area
.
Rem
emb
erth
atif
you
say
Con
tin
ued
onn
ext
page
30
Tab
le2
–C
onti
nu
edfr
ompr
evio
us
page
Var
iable
Surv
eyY
ears
Ques
tion
Wor
din
gR
esp
onse
Opti
ons
Nam
eC
over
ed
“much
mor
e”,
itm
ight
requir
e
ata
xin
crea
seto
pay
for
it
...“
Hea
lth”
spen
ded
uc
ISSP
RO
G19
85,
1990
,19
96,
[as
abov
e]“E
duca
tion
”5-
poi
nt
2006
,20
161
=sp
end
much
mor
e,5
=sp
end
much
less
spen
dol
dIS
SP
RO
G19
85,
1990
,19
96,
[as
abov
e]“O
ldA
geP
ensi
ons”
5-p
oint
2006
,20
161
=sp
end
much
mor
e,5
=sp
end
much
less
spen
dum
pIS
SP
RO
G19
85,
1990
,19
96,
[as
abov
e]“U
nem
plo
ym
ent
5-p
oint
2006
,20
16B
enefi
ts”
1=
spen
dm
uch
mor
e,5
=sp
end
much
less
taxhig
hin
cIS
SP
RO
G,
1987
,19
92,
1996
,T
axes
for
thos
ew
ith
hig
h5-
poi
nt
INE
Q20
06,
2016
inco
mes
are
too
hig
hor
too
1=
much
too
low
,5
=m
uch
too
hig
h
low
taxm
idin
cIS
SP
RO
G,
1987
,19
92,
1996
,T
axes
for
thos
ew
ith
mid
dle
5-p
oint
INE
Q20
06,
2016
inco
mes
are
too
hig
hor
too
1=
much
too
low
,5
=m
uch
too
hig
h
low
taxlo
win
cIS
SP
RO
G,
1987
,19
92,
1996
,T
axes
for
thos
ew
ith
low
5-p
oint
INE
Q20
06,
2016
inco
mes
are
too
hig
hor
too
1=
much
too
low
,5
=m
uch
too
hig
h
low
incd
iffIS
SP
INE
Q19
87,
1992
,19
99,
Diff
eren
ces
inin
com
ein
my
5-p
oint
2009
countr
yar
eto
ola
rge
1=
Str
ongl
yA
gree
,5
=Str
ongl
yD
isag
ree
less
ben
sIS
SP
INE
Q19
87,
1992
,T
he
gove
rnm
ent
shou
ldsp
end
5-p
oint
2009
less
onb
enefi
tsfo
rth
ep
oor
1=
Str
ongl
yD
isag
ree,
5=
Str
ongl
yA
gree
Con
tin
ued
onn
ext
page
31
Tab
le2
–C
onti
nu
edfr
ompr
evio
us
page
Var
iable
Surv
eyY
ears
Ques
tion
Wor
din
gR
esp
onse
Opti
ons
Nam
eC
over
ed
unip
oor
ISSP
INE
Q19
87,
1992
The
gove
rnm
ent
shou
ldpro
vid
e5-
poi
nt
mor
ech
ance
sfo
rch
ildre
nfr
om1
=st
rongl
yag
ree,
5=
stro
ngl
ydis
agre
e
poor
fam
ilie
sto
goto
univ
ersi
ty
govre
dis
tE
SS
2002
,04
,06
,08
,T
he
gove
rnm
ent
shou
ldta
ke5-
poi
nt
10,
12,
14,
16,
18m
easu
res
tore
duce
diff
eren
ces
in1
=ag
ree
stro
ngl
y,5
=dis
agre
est
rongl
y
inco
me
leve
ls
ben
seco
nE
SS
2008
,20
16Soci
alb
enefi
ts/s
ervic
espla
ce5-
poi
nt
too
grea
tst
rain
onec
onom
y1
=ag
ree
stro
ngl
y,5
=dis
agre
est
rongl
y
ben
sbus
ESS
2008
,20
16Soci
alb
enefi
ts/s
ervic
esco
st5-
poi
nt
busi
nes
ses
too
much
inta
xes
/1
=ag
ree
stro
ngl
y,5
=dis
agre
est
rongl
y
char
ges
ben
slaz
yE
SS
2008
,20
16Soci
alb
enefi
ts/s
ervic
esm
ake
5-p
oint
peo
ple
lazy
1=
agre
est
rongl
y,5
=dis
agre
est
rongl
y
unem
ptr
yE
SS
2008
,20
16-
Mos
tunem
plo
yed
peo
ple
do
5-p
oint
not
real
lytr
yto
find
ajo
b1
=ag
ree
stro
ngl
y,5
=dis
agre
est
rongl
y
enti
tled
ESS
2008
,20
16M
any
man
age
toob
tain
5-p
oint
ben
efits
/ser
vic
esnot
enti
tled
1=
agre
est
rongl
y,5
=dis
agre
est
rongl
y
to
taxhig
her
EB
PO
V20
09,
2010
Peo
ple
who
are
wel
l-off
shou
ldpay
4-p
oint
hig
her
taxes
soth
ego
vern
men
t1
=to
tally
agre
e,4
=to
tally
dis
agre
e
has
mor
em
eans
tofigh
tp
over
ty”
eqin
cent
EV
S19
90,
1999
-00,
“How
wou
ldyo
upla
ceyo
ur
vie
ws
”10
-poi
nt
Con
tin
ued
onn
ext
page
32
Tab
le2
–C
onti
nu
edfr
ompr
evio
us
page
Var
iable
Surv
eyY
ears
Ques
tion
Wor
din
gR
esp
onse
Opti
ons
Nam
eC
over
ed
2008
-10
onth
issc
ale?
1=
inco
mes
shou
ld
be
mad
em
ore
equal
,10
=w
enee
d
larg
erin
com
ediff
eren
ces
as
ince
nti
ves
free
firm
sE
VS
1999
-00,
[as
abov
e]1
=th
est
ate
shou
ld10
-poi
nt
2008
-10
contr
olfirm
sm
ore
effec
tive
ly
...1
0=
the
stat
esh
ould
give
mor
efr
eedom
tofirm
sto
pro
vid
e
for
them
selv
es
pro
vid
eE
VS
1990
,19
99-0
0,[a
sab
ove]
1=
the
gove
rnm
ent
10-p
oint
2008
-10
shou
ldta
kem
ore
resp
onsi
bilit
y
toen
sure
that
ever
yone
is
pro
vid
edfo
r,10
=p
eople
shou
ld
take
mor
eres
pon
sibilit
yfo
r
pro
vid
ing
for
them
selv
es
Table
S3:Variablesincluded
inth
eSocialand
PostmaterialIssu
esM
odel
Var
iable
Surv
eyY
ears
Ques
tion
Wor
din
gR
esp
onse
Opti
ons
Nam
eC
over
ed
mee
tings
ISSP
RO
G19
90,
1996
,T
her
ear
em
any
way
sp
eople
or4-
poi
nt
2006
,20
16or
ganis
atio
ns
can
pro
test
agai
nst
a1
=defi
nit
ely,
4=
defi
nit
ely
not
Con
tin
ued
onn
ext
page
33
Tab
le3
–C
onti
nu
edfr
ompr
evio
us
page
Var
iable
Surv
eyY
ears
Ques
tion
Wor
din
gR
esp
onse
Opti
ons
Nam
eC
over
ed
gove
rnm
ent
acti
onth
eyst
rongl
yop
pos
e.
Ple
ase
show
whic
hyo
uth
ink
shou
ldb
e
allo
wed
and
whic
hsh
ould
not
be
allo
wed
:“O
rgan
izin
gpublic
mee
tings
topro
test
agai
nst
the
gove
rnm
ent’
pro
test
sIS
SP
RO
G19
90,
1996
,[a
sab
ove]
“Org
anis
ing
pro
test
mar
ches
4-p
oint
2006
,20
16an
ddem
onst
rati
ons”
1=
defi
nit
ely,
4=
defi
nit
ely
not
kid
job
ISSP
F+
G,
1988
,19
94,
To
what
exte
nt
do
you
agre
eor
5-p
oint
2002
,20
12,
dis
agre
e...?
“Apre
-sch
ool
child
is1
=st
rongl
ydis
agre
e,5
=st
rongl
yag
ree
like
lyto
suff
erif
his
orher
mot
her
wor
ks”
wor
km
oth
ISSP
F+
G19
88,
1994
,[a
sab
ove]
“Aw
orkin
gm
other
can
5-p
oint
2002
,20
12es
tablish
just
asw
arm
and
secu
rea
1=
stro
ngl
yag
ree,
5=
stro
ngl
ydis
agre
e
rela
tion
ship
wit
hher
childre
nas
a
mot
her
who
does
not
wor
k”
fam
job
ISSP
F+
G19
88,
1994
,[a
sab
ove]
“All
inal
l,fa
mily
life
5-p
oint
2002
,20
12su
ffer
sw
hen
the
wom
anhas
a1
=st
rongl
ydis
agre
e,5
=st
rongl
yag
ree
full-t
ime
job”
hou
sew
ife
ISSP
F+
G19
88,
1994
,[a
sab
ove]
“Bei
ng
ahou
sew
ife
isju
stas
5-p
oint
2002
,20
12fu
lfillin
gas
wor
kin
gfo
rpay
”1
=st
rongl
ydis
agre
e,5
=st
rongl
yag
ree
husb
wif
eIS
SP
F+
G19
88,
1994
,[a
sab
ove]
“Am
an’s
job
isto
earn
mon
ey5-
poi
nt
2002
,20
12a
wom
an’s
job
isto
look
afte
rth
e1
=st
rongl
ydis
agre
e,5
=st
rongl
yag
ree
Con
tin
ued
onn
ext
page
34
Tab
le3
–C
onti
nu
edfr
ompr
evio
us
page
Var
iable
Surv
eyY
ears
Ques
tion
Wor
din
gR
esp
onse
Opti
ons
Nam
eC
over
ed
hom
ean
dfa
mily”
singp
aren
tIS
SP
F+
G19
88,
1994
,[a
sab
ove]
“One
par
ent
can
bri
ng
up
a5-
poi
nt
2002
,20
12ch
ild
asw
ell
astw
opar
ents
toge
ther
”1
=st
rongl
yag
ree,
5=
stro
ngl
ydis
agre
e
wor
kkid
ISSP
F+
G19
88,
1994
,D
oyo
uth
ink
that
wom
ensh
ould
wor
k3-
poi
nt
2002
,20
12w
ork
outs
ide
the
hom
efu
ll-t
ime,
1=
wor
kfu
ll-t
ime,
3=
stay
athom
e
par
t-ti
me
ornot
atal
lunder
the
follow
ing
circ
um
stan
ces?
“When
ther
eis
ach
ild
under
school
age”
wor
ksc
hool
ISSP
F+
G19
88,
1994
,[a
sab
ove]
“Aft
erth
eyo
unge
stch
ild
3-p
oint
2002
,20
12st
arts
school
”1
=w
ork
full-t
ime,
3=
stay
athom
e
envfu
ture
ISSP
EN
V19
93,
1990
,H
owm
uch
do
you
agre
eor
dis
agre
e5-
poi
nt
2010
wit
hea
chof
thes
est
atem
ents
...
1=
stro
ngl
ydis
agre
e,5
=st
rongl
yag
ree
“we
wor
ryto
om
uch
abou
tth
efu
ture
of
the
envir
onm
ent
and
not
enou
ghab
out
pri
ces
and
jobs
today
”
envpro
gIS
SP
EN
V19
93,
1990
,[a
sab
ove]
“Peo
ple
wor
ryto
om
uch
5-p
oint
2010
abou
thum
anpro
gres
shar
min
gth
e1
=st
rongl
ydis
agre
e,5
=st
rongl
yag
ree
envir
onm
ent”
envhta
xIS
SP
EN
V19
93,
1990
,H
oww
illing
wou
ldyo
ub
eto
pay
much
5-p
oint
2010
hig
her
taxes
inor
der
topro
tect
the
1=
very
willing,
5=
very
unw
illing
envir
onm
ent?
envst
dIS
SP
EN
V19
93,
1990
,H
oww
illing
wou
ldyo
ub
eto
acce
pt
cuts
5-p
oint
Con
tin
ued
onn
ext
page
35
Tab
le3
–C
onti
nu
edfr
ompr
evio
us
page
Var
iable
Surv
eyY
ears
Ques
tion
Wor
din
gR
esp
onse
Opti
ons
Nam
eC
over
ed
2010
inyo
ur
stan
dar
dof
livin
gin
order
to1
=ve
ryw
illing,
5=
very
unw
illing
pro
tect
the
envir
onm
ent?
envla
ws
ISSP
EN
V19
93,
1990
,If
you
had
toch
oos
e,w
hic
hof
the
2-p
oint
2010
follow
ing
wou
ldb
ecl
oses
tto
your
vie
ws?
1=
seco
nd
stat
emen
t,
vie
ws?
“gov
ernm
ent
shou
ldle
tor
din
ary
2=
firs
tst
atem
ent
peo
ple
dec
ide
for
them
selv
eshow
to
pro
tect
the
envir
onm
ent,
even
ifit
mea
ns
they
don
’tal
way
sdo
the
righ
tth
ing”
,or
“gov
ernm
ent
shou
ldpas
sla
ws
tom
ake
ordin
ary
peo
ple
pro
tect
the
envir
onm
ent
,ev
enif
itin
terf
eres
wit
hp
eople
’sri
ghts
tom
ake
thei
row
ndec
isio
ns”
envbla
ws
ISSP
EN
V19
93,
1990
,[A
sab
ove
-su
bst
itute
2-p
oint
2010
“busi
nes
ses”
for
“ord
inar
yp
eople
”]1
=se
cond
stat
emen
t,
2=
firs
tst
atem
ent
kid
job1
EV
S19
90,
1999
-00,
To
what
exte
nt
do
you
agre
eor
4-p
oint
2008
-10
dis
agre
e...?
“Apre
-sch
ool
child
is1
=st
rongl
ydis
agre
e,4
=st
rongl
yag
ree
like
lyto
suff
erif
his
orher
mot
her
wor
ks”
envta
x1
EV
S19
90,
1999
-00,
Iam
now
goin
gto
read
out
som
e3-
poi
nt
2008
-10
stat
emen
tsab
out
the
envir
onm
ent.
1=
stro
ngl
yag
ree,
4=
stro
ngl
ydis
agre
e
For
each
one
read
out,
can
you
tell
me
Con
tin
ued
onn
ext
page
36
Tab
le3
–C
onti
nu
edfr
ompr
evio
us
page
Var
iable
Surv
eyY
ears
Ques
tion
Wor
din
gR
esp
onse
Opti
ons
Nam
eC
over
ed
whet
her
you
agre
est
rongl
y,ag
ree,
dis
agre
eor
stro
ngl
ydis
agre
e?“I
wou
ld
agre
eto
anin
crea
sein
taxes
ifth
eex
tra
mon
eyis
use
dto
pre
vent
envir
onm
enta
l
pol
luti
on”
auth
orit
yE
VS
1981
-2,
1990
Her
eis
alist
ofva
riou
sch
ange
sin
our
3-p
oint
1999
-00,
2008
-10
way
oflife
that
mig
ht
take
pla
cein
the
1=
good
thin
g,3
=bad
thin
g
nea
rfu
ture
.P
leas
ete
llm
efo
rea
chon
e,
ifit
wer
eto
hap
pen
whet
her
you
thin
kit
wou
ldb
ea
good
thin
g,a
bad
thin
g,or
don
’tyo
um
ind?
jobsc
arce
EV
S19
90,
1999
-00,
Do
you
dis
agre
eor
agre
ew
ith
the
3-p
oint
2008
-10
follow
ing
stat
emen
ts:
“When
jobs
are
1=
dis
agre
e,3
=ag
ree
scar
ce,
men
hav
em
ore
righ
tto
ajo
b
than
wom
en”
singp
aren
t1E
VS
1981
-2,
1990
[as
abov
e]“I
fso
meo
ne
says
ach
ild
nee
ds
3-p
oint
1999
-00,
008-
10a
hom
ew
ith
bot
ha
fath
eran
da
mot
her
1=
dis
agre
e,3
=ag
ree
togr
owup
hap
pily,
wou
ldyo
ute
nd
to
agre
eor
dis
agre
e?
wor
km
oth1
EV
S19
90,
1999
-00,
Peo
ple
talk
abou
tth
ech
angi
ng
role
sof
4-p
oint
2008
-10
men
and
wom
ento
day
.F
orea
chof
the
1=
agre
est
rongl
y,4=
dis
agre
est
rongl
y
follow
ing
stat
emen
tsI
read
out,
can
you
Con
tin
ued
onn
ext
page
37
Tab
le3
–C
onti
nu
edfr
ompr
evio
us
page
Var
iable
Surv
eyY
ears
Ques
tion
Wor
din
gR
esp
onse
Opti
ons
Nam
eC
over
ed
tell
me
how
much
you
agre
ew
ith
each
:
“Aw
orkin
gm
other
can
esta
blish
just
as
war
man
dse
cure
are
lati
onsh
ipw
ith
her
childre
nas
am
other
who
does
not
wor
k”
hw
inco
me
EV
S19
90,
1999
-00,
[as
abov
e]“B
oth
the
husb
and
and
wif
e4-
poi
nt
2008
-10
shou
ldco
ntr
ibute
tohou
sehol
din
com
e1
=ag
ree
stro
ngl
y,4=
dis
agre
est
rongl
y
hom
osex
EV
S19
81-2
,19
90“P
leas
ete
llm
efo
rea
chof
the
follow
ing
10-p
oint
1999
-00,
2008
-10
stat
emen
tsw
het
her
you
thin
kit
can
1=
alw
ays
just
ified
,
alw
ays
be
just
ified
,nev
erb
eju
stifi
ed,
or10
=nev
erju
stifi
ed
som
ethin
gin
bet
wee
n:
“hom
osex
ual
ity”
abor
tion
EV
Sas
abov
e[a
sab
ove]
“Ab
orti
on”
asab
ove
div
orce
EV
Sas
abov
e[a
sab
ove]
“Div
orce
”as
abov
e
euth
anE
VS
asab
ove
[as
abov
e]“E
uth
anas
ia(t
erm
inat
ing
asab
ove
the
life
ofth
ein
cura
bly
sick
)”
pot
use
EV
Sas
abov
e[a
sab
ove]
“Tak
ing
the
dru
gm
arij
uan
aas
abov
e
orhas
his
h”
trad
itio
nE
SS
2002
,04
,06
,08
,“I
tis
imp
orta
nt
totr
yto
follow
the
6-p
oint
10,
12,
14,
16,
18cu
stom
shan
ded
dow
nby
religi
onor
top
3re
spon
ses
indic
ate
agre
emen
t
fam
ily”
stro
ngg
ovE
SS
2002
,04
,06
,08
,“I
tis
imp
orta
nt
that
the
6-p
oint
10,
12,
14,
16,
18go
vern
men
tis
stro
ng
and
ensu
res
top
3re
spon
ses
indic
ate
dis
agre
emen
t
safe
ty”
Con
tin
ued
onn
ext
page
38
Tab
le3
–C
onti
nu
edfr
ompr
evio
us
page
Var
iable
Surv
eyY
ears
Ques
tion
Wor
din
gR
esp
onse
Opti
ons
Nam
eC
over
ed
gayri
ghts
ESS
2002
,04
,06
,08
,“T
ow
hat
exte
nt
do
you
agre
eor
5-p
oint
10,
12,
14,
16,
18dis
agre
eth
atga
ym
enan
dle
sbia
ns
1=ag
ree
stro
ngl
y,5=
dis
agre
est
rongl
y
shou
ldb
efr
eeto
live
thei
rlife
as
they
wis
h?
over
thro
wE
SS
2002
,04
,06
,08
,“T
ow
hat
exte
nt
do
you
agre
eor
5-p
oint
10dis
agre
eth
atp
olit
ical
par
ties
that
wis
h1=
dis
agre
est
rongl
y,5=
agre
est
rongl
y
toov
erth
row
dem
ocr
acy
shou
ld
be
ban
ned
?
hom
osex
1P
EW
2002
,07
,11
,13
,“H
omos
exual
ity
isa
way
oflife
that
2-p
oint
shou
ldb
eac
cepte
dby
soci
ety”
1=ag
ree,
2=dis
agre
e
envgr
owth
PE
W20
02,
07,
08,
09,
“Pro
tect
ing
the
envir
onm
ent
shou
ldb
e4-
poi
nt
10gi
ven
pri
orit
y,ev
enif
itca
use
s1=
com
ple
tely
agre
e,
slow
ergr
owth
and
som
elo
ssof
jobs”
4=co
mple
tely
dis
agre
e
Table
S4:Variablesincluded
inth
eIm
migra
tion
Model
Var
iable
Surv
eyY
ears
Ques
tion
Wor
din
gR
esp
onse
Opti
ons
Nam
eC
over
ed
trad
sIS
SP
NI
1995
,20
03,
2013
How
much
do
you
agre
eor
dis
agre
e5-
poi
nt
wit
hth
efo
llow
ing
stat
emen
ts?
“It
is1=
dis
agre
est
rongl
y,5=
agre
est
rongl
y
isim
pos
sible
for
peo
ple
who
do
not
shar
eth
isco
untr
y’s
cust
oms
Con
tin
ued
onn
ext
page
39
Tab
le4
–C
onti
nu
edfr
ompr
evio
us
page
Var
iable
Surv
eyY
ears
Ques
tion
Wor
din
gR
esp
onse
Opti
ons
Nam
eC
over
ed
and
trad
itio
ns
tob
ecom
efu
lly
[nat
ional
ity]”
imm
crim
eIS
SP
NI
1995
,20
03,
2013
[as
abov
e]“I
mm
igra
nts
incr
ease
5-p
oint
crim
era
tes”
1=dis
agre
est
rongl
y,5=
agre
est
rongl
y
imm
econ
ISSP
NI
1995
,20
03,
2013
[as
abov
e]“I
mm
igra
nts
are
gener
ally
5-p
oint
good
for
this
countr
y’s
econ
omy’
1=ag
ree
stro
ngl
y,5=
dis
agre
est
rongl
y
take
jobs
ISSP
NI
1995
,20
03,
2013
[as
abov
e]“I
mm
igra
nts
take
jobs
5-p
oint
away
from
peo
ple
who
wer
eb
orn
in1=
agre
est
rongl
y,5=
dis
agre
est
rongl
y
this
countr
y.”
imm
pro
veIS
SP
NI
2003
,20
13[a
sab
ove]
“Im
mig
rants
impro
ve5-
poi
nt
this
soci
ety
by
bri
ngi
ng
innew
1=ag
ree
stro
ngl
y,5=
dis
agre
est
rongl
y
idea
san
dcu
lture
s”
lega
lrig
hts
ISSP
NI
2003
,20
13[a
sab
ove]
“Leg
alim
mig
rants
to5-
poi
nt
this
countr
yw
ho
are
not
1=ag
ree
stro
ngl
y,5=
dis
agre
est
rongl
y
citi
zens
shou
ldhav
eth
esa
me
righ
tsas
citi
zens”
imp
orts
ISSP
NI
1995
,20
03,
2013
[as
abov
e]“W
esh
ould
lim
itth
e5-
poi
nt
imp
ort
offo
reig
npro
duct
sin
1=dis
agre
est
rongl
y,5=
agre
est
rongl
y
order
topro
tect
the
nat
ional
econ
omy”
imm
good
ESS
2002
,04
,06
,08
,Is
itge
ner
ally
good
orbad
for
the
11-p
oint
10,1
2,14
,16,
18co
untr
y’s
econ
omy
that
1=go
od,
11=
bad C
onti
nu
edon
nex
tpa
ge
40
Tab
le4
–C
onti
nu
edfr
ompr
evio
us
page
Var
iable
Surv
eyY
ears
Ques
tion
Wor
din
gR
esp
onse
Opti
ons
Nam
eC
over
ed
peo
ple
com
eto
live
her
efr
om
other
countr
ies?
imm
cult
ESS
2002
,04
,06
,08
,Is
the
countr
y’s
cult
ura
llife
11-p
oint
10,1
2,14
,16,
18ge
ner
ally
under
min
edor
1=en
rich
ed,
11=
under
min
ed
enri
ched
by
imm
igra
nts
com
ing
to
live
her
e?
imm
bet
ter
ESS
2002
,04
,06
,08
,Is
the
countr
ya
bet
ter
11-p
oint
10,1
2,14
,16,
18or
wor
sepla
ceto
live
asa
1=en
rich
ed,
11=
under
min
ed
resu
ltof
imm
igra
nts
com
ing
to
live
her
e?
imm
sam
eE
SS
2002
,04
,06
,08
,T
ow
hat
exte
nt
do
you
thin
kth
is4-
poi
nt
10,1
2,14
,16,
18co
untr
ysh
ould
allo
wp
eople
top
3si
gnal
agre
emen
t
ofth
esa
me
race
oret
hnic
grou
p
asm
ost
ofth
eco
untr
yto
com
ean
d
live
her
e?
imm
diff
ESS
2002
,04
,06
,08
,[a
sab
ove]
...d
iffer
ent
race
or4-
poi
nt
10,1
2,14
,16,
18et
hnic
grou
p?
top
3si
gnal
agre
emen
t
imm
poor
ESS
2002
,04
,06
,08
,[a
sab
ove]
...p
eople
from
the
4-p
oint
10,1
2,14
,16,
18p
oor
erco
untr
ies
outs
ide
Euro
pe?
top
3si
gnal
agre
emen
t
scar
ceim
ms
EV
S19
90,
1999
-00,
‘When
jobs
are
scar
ce,
emplo
yers
3-p
oint
2008
-10
shou
ldgi
vepri
orit
yto
nat
ive
1=dis
agre
e,3=
agre
e
peo
ple
over
imm
igra
nts
’
Con
tin
ued
onn
ext
page
41
Tab
le4
–C
onti
nu
edfr
ompr
evio
us
page
Var
iable
Surv
eyY
ears
Ques
tion
Wor
din
gR
esp
onse
Opti
ons
Nam
eC
over
ed
conci
mm
sE
VS
1999
-00,
To
what
exte
nt
do
you
feel
5-p
oint
2008
-10
conce
rned
abou
tth
elivin
g1=
very
much
,5=
not
atal
l
condit
ions
ofim
mig
rants
in
your
countr
y?
lim
itfo
rsE
VS
1999
-00,
‘Whic
hon
eof
the
follow
ing
do
4-p
oint
2008
-10
you
thin
kth
ego
vern
men
tsh
ould
top
2:pro
hib
it/p
lace
lim
its
do
abou
tp
eople
from
less
dev
elop
edco
untr
ies
com
ing
her
e
tow
ork?
fori
nfl
PE
W20
02,
07,
‘Our
way
oflife
nee
ds
tob
e4-
poi
nt
09,
12pro
tect
edag
ainst
fore
ign
1=co
mple
tely
dis
agre
e,
influen
ce’
4=co
mple
tely
agre
e
socr
ight
EB
VA
L19
97,
2000
,‘L
egal
lyes
tablish
edim
mig
rants
2-p
oint
2003
from
outs
ide
the
Euro
pea
n0=
agre
e,1=
dis
agre
e
Unio
nsh
ould
hav
eth
esa
me
soci
alri
ghts
asci
tize
ns.
’
sendum
pE
BV
AL
1997
,20
00,
‘Leg
ally
esta
blish
edim
mig
rants
2-p
oint
2003
from
outs
ide
the
Euro
pea
n0=
dis
agre
e,1=
agre
e
Unio
nsh
ould
be
sent
bac
kto
thei
rco
untr
yof
orig
inif
they
are
unem
plo
yed.’
sendal
lE
BV
AL
1997
,20
00,
‘Leg
ally
esta
blish
edim
mig
rants
2-p
oint
Con
tin
ued
onn
ext
page
42
Tab
le4
–C
onti
nu
edfr
ompr
evio
us
page
Var
iable
Surv
eyY
ears
Ques
tion
Wor
din
gR
esp
onse
Opti
ons
Nam
eC
over
ed
2003
from
outs
ide
the
Euro
pea
n0=
dis
agre
e,1=
agre
e
Unio
nsh
ould
all
be
sent
bac
k
toth
eir
countr
yof
orig
in’
allh
ome
EB
VA
L19
97,
2000
,‘A
llille
gal
imm
igra
nts
shou
ldb
e2-
poi
nt
2003
sent
bac
kto
thei
rco
untr
yof
orig
in0=
dis
agre
e,1=
agre
e
wit
hou
tex
cepti
on’
43
3 Analysis of Individual Survey Items in the Relative
Economic Issues Scales
As discussed in the main text, the finding that young people have become more right-wing
on tax and spending over the past two decades may be surprising. Here I show that this
finding faithfully reflects patterns in the underlying survey data, both in aggregate and for
individual countries. I do this using the two sets of surveys questions with the longest
temporal coverage: the ISSP Role of Government and Inequality surveys. The former were
carried out in 1985, 1990, 1996, 2006 and 2016 and the latter in 1987, 1992, 1999 and 2009.
Figures S1 and S2 display over-time trends in age differences for the main tax, spending and
inequality questions from these surveys: the variable names in the figures match those in
Table S2 above.
−0.4
−0.2
0.0
0.2
1990 2000 2010
Age
Diff
eren
ce in
Con
serv
atis
m (
Old
min
us Y
oung
)
Spending Item
spendhealth
spendeduc
spendold
spendump
Figure S1: Differences in mean preference between old and young for higher governmentspending in four policy areas (ISSP Role of Government Surveys), and associated 95% con-fidence intervals [positive = old people more conservative]
44
In both figures, the old are defined as those aged 58-77 (the two oldest groups in the main
paper’s analysis) and the young as those aged 18-37 (the two youngest groups), as in Figure
2 of the main paper.9 They include the average for all available coutnries in each year;
individual countries are shown below. Negative numbers imply that the young are more
conservative (opposed to higher spending). Figure S1 shows that in the 1980s and early
1990s, across most issues the old were at least as conservative, or more conservative, than
the young. But over the 1990s to the 2000s, the young became relatively more conservative,
preferring lower spending on health and unemployment as well as pensions. They also
became relatively more conservative on both education and pensions spending, although on
education they have, unsurprisingly, always preferred higher spending to the old. These
patterns closely match those in Figure 1 of the main text: the age gap in ideology was small
in the 1980s and early 1990s but widened by the late 1990s, remaining high throughout the
2000s and 2010s.
Figure S2 shows similar patterns for questions on inequality, benefits spending and pro-
gressive taxation from the ISSP Inequality surveys. These again show, overall, a pattern of
increasing age gaps in conservatism from the 1980s to the late 2000s. The old were more
conservative than the young on benefits spending in the 1980s (Lessbens), but became more
conservative thereafter. On taxing those with high incomes, the young became considerably
more conservative, relative to the old, over the same period (Taxhighinc). On perceptions
of inequality (Incdiff ), the young were more conservative in all periods and this grew over
the 2000s, but there was no aggregate change over the whole period. Again, taken together,
these closely match the estimates in the main paper which show a large age gap from the
1990s onward.
Figures S1-2 are not balanced samples. Different countries appear in different years,
although the coverage becomes increasingly comprehensive over time. In case these patterns
are affected by the countries that happen to appear in a given year, Figures S3 and S4 look at
all available countries and years for each measure, presenting the same age gap in ideology as
in Figures S1-2. Although there is some variation, in the majority of cases the same patterns
occur within countries: age gaps were larger from the late 1990s onwards, and particularly
in the 2000s, compared to the 1980s and early 1990s.
Figure S3 also shows that age gaps for most of these measures are relatively modest.
This is particularly the case for health, where most point estimates are centered very close
to zero, despite the elderly using more healthcare on average than the young. The evidence
for education points to a modest age gap in the expected direction, although the differences
9. The results do not differ if single age groups are used. However, this results in very small groups forestimation by country, which appears below.
45
−0.2
0.0
0.2
1990 1995 2000 2005 2010
Age
Diff
eren
ce in
Con
serv
atis
m (
Old
min
us Y
oung
)
Survey Item
Lessbens
Taxhighinc
Incdiff
Figure S2: Differences in mean preference between old and young on inequality, benefitsspending and progressive taxation (ISSP Inequaity Surveys), and associated 95% confidenceintervals [positive = old people more conservative]
are generally very small. The mean cross-country age gap of 0.101 for the countries shown
in the figure corresponds to one eighth of a standard deviation of the question’s response
scale. In fact, baseline support for greater education spending is very high across both age
groups. The only difference is in intensity, with the young slightly more likely to want ‘much
higher’ spending. In 2016 42.9% and 28.8% of those aged 30 and under wanted ‘higher’ and
‘much higher’ education spending, compared to 44.9% and 21.8% of those aged 65 and over.
Even in France where the 2016 gap is highest, a majority (53%) of the elderly supported
higher or much higher spending, compared to 65% of the young. inally, for spending on
pensions the average age difference is a bit larger, with the old more likely to want higher
spending. Again though, virtually nobody – young or old – wanted lower spending: 4.4% of
the young and 1.3% of the old in 2016. Majorities of both age groups again favored higher
spending, with 44.8% and 18.1% of the young wanting it higher and much higher compared
46
to 41.4% and 31.4% of the old. Age differences in preferences for ‘much higher’ pensions
spending are therefore large, but otherwise there is quite substantial agreement. Pensions
spending remains a popular spending item across age groups. It is arguably important to
distinguish between areas where the young and old have preferences for completely different
policies versus areas where they want the same things, but with differing degrees of intensity.
Education and pensions spending, classic valence items, clearly fall into the latter camp.
47
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Pensions Unemployment
Health Education
−1.0 −0.5 0.0 0.5 −1.0 −0.5 0.0 0.5
SwitzerlandSweden
SpainSloveniaSlovakiaPortugal
PolandNorway
NetherlandsLithuania
LatviaItaly
IrelandHungary
GermanyGreat Britain
FranceFinland
DenmarkCzech Republic
CyprusBulgariaBelgiumAustria
SwitzerlandSweden
SpainSloveniaSlovakiaPortugal
PolandNorway
NetherlandsLithuania
LatviaItaly
IrelandHungary
GermanyGreat Britain
FranceFinland
DenmarkCzech Republic
CyprusBulgariaBelgiumAustria
Old versus Young (Difference in Means)
Survey Year
●
●
●
●
●
2016
2006
1996
1990
1985
Figure S3: Differences in mean preference between old and young for higher governmentspending in four policy areas (ISSP Role of Government Surveys), and associated 95% con-fidence intervals, by country and year
49
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Lessbens Taxhighinc Incdiff
−1.0 −0.5 0.0 0.5 −1.0 −0.5 0.0 0.5 −1.0 −0.5 0.0 0.5
Switzerland
Sweden
Spain
Slovenia
Slovakia
Portugal
Poland
Norway
Netherlands
Lithuania
Latvia
Italy
Hungary
Germany
Great Britain
France
Finland
Estonia
Denmark
Czech Republic
Cyprus
Bulgaria
Austria
Old versus Young (Difference in Means)
Survey Year
●
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●
2009
1999
1992
1987
Figure S4: Differences in mean preference between old and young on inequality, benefitsspending and progressive taxation (ISSP Inequality Surveys), and associated 95% confidenceintervals, by country and year
4 Trends in Conservatism by Age Group and European
Region
Absolute Economic Relative Economic
Social Immigration
1980 1990 2000 2010 1980 1990 2000 2010
−1
0
1
−1
0
1
Con
serv
atis
m
Age
18−27
28−37
38−47
48−57
58−67
68−77
Figure S5: Trends in conservatism over time by age group and issue domain in WesternEurope, 1981-82 to 2017-18.
50
Absolute Economic Relative Economic
Social Immigration
1980 1990 2000 2010 1980 1990 2000 2010
−1
0
1
−1
0
1
Con
serv
atis
m
Age
18−27
28−37
38−47
48−57
58−67
68−77
Figure S6: Trends in conservatism over time by age group and issue domain in EasternEurope, 1981-82 to 2017-18.
Western Europe = Austria, Belgium, Denmark, Finland, France, Germany, Great Britain,
Ireland, Italy, Netherlands, Northern Ireland, Norway, Portugal, Spain, Sweden, Switzerland
Eastern Europe = Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland,
Slovakia, Slovenia
51
